Systems and methods for non-contact tracking and analysis of physical activity using imaging

ABSTRACT

Systems and methods for tracking and analysis of physical activity is disclosed. In some aspects, a provided method includes receiving a time sequence of images captured while an individual is performing the physical activity, and generating, using the time sequence of images, at least one map indicating a movement of the individual. The method also includes identifying at least one body portion using the at least one map, and computing at least one index associated with the identified body portions to characterize the physical activity of the individual. The method further includes generating a report using the at least one index.

CROSS REFERENCE TO RELATED APPLICATIONS

This is a continuation application that claims benefit to U.S. patentapplication Ser. No. 16/059,904, filed on Aug. 9, 2018, which is acontinuation of U.S. patent application Ser. No. 14/823,364, filed onAug. 11, 2015, which is based on, claims priority to, and incorporatesherein by reference in its entirety U.S. Provisional Application Ser.No. 62/035,675, filed Aug. 11, 2014, and entitled SYSTEM AND METHOD FORNON-CONTACT TRACKING AND ANALYSIS OF EXERCISE.

FIELD

The field of the present disclosure is related to monitoring movement ofan individual. More particularly, the present disclosure generallyrelates to obtaining and analyzing data associated with predefinedmovements of an individual using non-contacting equipment.

BACKGROUND

The importance of physical activity is widely accepted, withoverwhelming evidence pointing to the fact that regular exercise canhelp lower excess weight, reduce disease risks, and improve overallhealth. With increasingly sedentary lifestyles, maintaining regularworkouts continues to be a challenge for many people. As a result, anumber of tools and devices have been developed in recent years toquantify and track personal activities. Such devices can be used tomotivate individuals to adhere to specific workout regimens, as well asprovide valuable information to health and fitness professionals toidentify the most beneficial course of action. In addition, such toolscan enable study of the impact of specific activities on disease.

Assessment of physical activity has been traditionally based onself-reporting, and more recently on portable or wearable electronicdevices. With self-reporting, various databases listing activityinformation obtained from population studies have been used to estimateenergy expenditure during particular exercises. This approach isburdensome, subjective and prone to human error. With the advent ofsmartphones and other personal devices, personalized tracking ofphysical activity has become easier. Although such portable or wearabledevices, fitted with a number of physical sensors, such asaccelerometers and GPS trackers, offer distinct advantages in estimatingphysical activity level compared to self-reporting, they also havedrawbacks.

Specifically, GPS tracking methods are limited to certain outdooractivities, such as running, cycling or hiking. On the other hand,accelerometer-based tracking methods are sensitive to how they areutilized, for instance, whether carried in a pocket or worn on an arm.They also require accurate algorithms for determining true energyexpenditure, and differentiating non-exercise induced movements, such asdriving or riding a bus. More importantly, many of the above-mentionedtechnologies cannot be applied to many common physical activities,including popular workouts (e.g., push up, yoga and weight lifting), aswell as housework activities, and so forth. In addition, most wearabledevices determine complex human body movements based on measurement witha single sensor at a particular location of the body (e.g., wrist),which can result in a number of false readings. Moreover, a device wornon a wrist, for example, cannot distinguish between bicep training andsay eating a potato chip, since both activities involve similar armmovement.

As an alternative to wearable devices, imaging-based systems, relying onradio waves and optical imaging, have been developed to provideinformation for determining energy use during physical activity.Although these systems rely on sensors not directly in contact with anindividual, in order to accurately track body movement, special markersplaced at strategic locations, such as joints, must be worn. This makesuse of imaging-based systems inconvenient for most people. In addition,such technologies have focused primarily on physical activitiesinvolving large center-of-mass movements, such as walking or running. Bycontrast, many common indoor workout routines, including push-ups,sit-ups, jumping jacks, and squats, involve small or subtle bodymovements (e.g., arms, legs and head), and also often upward movementsagainst gravity, which are hard to track. Therefore, opticalimaging-based activity trackers are not typically used for trackingexercise.

In light of the above, there is a need for improved systems and methodsto accurately measure various characteristics associated with commonphysical activities, such as exercise. It is with these observations inmind, among others, that various aspects of the present disclosure wereconceived and developed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic diagram of an example system in accordance withaspects of the present disclosure.

FIG. 1B are images showing an example velocity fields generated usingmethods of the present disclosure.

FIG. 1C is example output for a smartphone utilizing methods inaccordance with the present disclosure.

FIG. 1D is a schematic diagram of an example video recording device foranalyzing exercise in accordance with the present disclosure.

FIG. 2A is a flowchart setting forth steps of a process forcharacterizing physical exercise, in accordance with aspects of thepresent disclosure.

FIG. 2B is a schematic diagram illustration an example video recordingimplementation, in accordance with aspects of the present disclosure.

FIG. 3 shows a process for the use of a hierarchical kinematic algorithmfor analyzing exercises in accordance with the present disclosure.

FIG. 4 shows an oriented histogram of a push-up in accordance with thepresent disclosure.

FIG. 5 shows charts of the amplitude of main optical flow over time forvarious exercises in accordance with the present disclosure.

FIG. 6 is a graphical illustration showing the determination ofrepetition count of an exercise using the boundary of an individual inaccordance with the present disclosure.

FIG. 7 shows counting the repetitions of an exercise using templatematching in accordance with the present disclosure.

FIG. 8 shows vertical displacement and velocity square averaged overeach repetitive cycle for a sit-up and a push-up in accordance with thepresent disclosure.

FIG. 9 shows kinetic and potential energy analyses of a sit-up inaccordance with the present disclosure.

FIG. 10 shows kinetic and potential energy analyses of a jumping jack inaccordance with the present disclosure.

FIG. 11 shows a weighted kinetic and potential energy analysis of ajumping jack in accordance with the present disclosure.

Corresponding reference characters indicate corresponding elements amongthe view of the drawings. The headings used in the figures do not limitthe scope of the claims.

DETAILED DESCRIPTION

The present disclosure provides systems and methods directed to anon-contacting approach for assessing exercise, and other physicalactivities. Specifically, methods described are based on processingimages, and other data, acquired from an individual to objectivelyquantify physical activity. As will be described, an optical flowapproach may be utilized to analyze body movement to determine variousindices associated with exercise, including activity intensity andenergy expenditure. In some aspects, a hierarchical kinematic approachmay be implemented to analyze body movement using various degrees ofdetail, reflecting particular body parts or body regions, or layers, asreferred to herein. For example, a first layer may be associated with anoverall body movement, namely a center of mass, while a second layer mayinclude the head, trunk, legs and arms of the body. A third layer mayreflect body portions thereof, and so forth, based on need and/oravailable image quality, as well as other factors.

In some aspects, velocity as well as displacement, such as verticaldisplacement, of different body portions may be analyzed during one ormore physical activities. Specifically, velocity is related to kineticenergy, while vertical displacement is associated with potential energy.As may be appreciated, this approach accounts for importance of gravity.In particular, vertical displacement is especially important for manypopular indoor workouts, which sometimes involve carrying additionalweight. The intensity of a physical activity may then be quantifiedusing the various weightings of contributions from such potential andkinetic terms from different body parts or portions thereof. In someimplementations, systems and methods provided herein may be utilized toautomatically identify and characterize a particular physical activity,using measures, as described.

Although the present disclosure, describes the present systems andmethods with reference to specific implementations, it may readily berecognized that this approach may be extended to a variety ofapplications. For example, rather than analyzing exercise, the providedsystems and methods may also be utilized to determine a workerefficiency, a stress level, an activity accuracy or a likelihood forfatigue. In addition, systems and methods herein may also be combinedwith measurements physiological parameters, such as heart rate, stress,breathing rate, blood oxygen saturation, and so forth, providing anindication of a level of fitness or health.

Turning now to FIG. 1, a block diagram of a system 100 for use inidentifying and characterizing physical activities is shown. In general,the system 100 may be any device, apparatus or system configured forcarrying out instructions in accordance with the present disclosure.System 100 may operate independently or as part of, or in collaborationwith, a computer, system, device, machine, mainframe, or server. In someaspects, the system 100 may be portable, such as a mobile device,smartphone, tablet, laptop, or other portable or wearable device orapparatus, and can be connected to the internet physically orwirelessly. In this regard, the system 100 may be any system that isdesigned to integrate with a variety of software and hardwarecapabilities and functionalities, and may be capable of operatingautonomously and/or with instruction from a user or other system ordevice. In accordance with the present disclosure, system 100 may beconfigured to identify and characterize a physical activity performed byan individual without contacting the individual. As such, system 100 maybe preferably positioned away from the individual, although certainportions of the system 100 could remain in contact with the individual.

In general, the system 100 may include a processor 102, a memory 104, aninput 106, an output 108, a camera 110, or similar image or videorecording device or apparatus, and optionally other physiologicalsensors 112. In particular, the input 106 may be configured to receive avariety of information from a user, a server, a database, and so forth,via a wired or wireless connection. For example, the input 106 may be inthe form of one or more touch screen, button, keyboard, mouse, and thelike, as well as a compact disc, a flash-drive or othercomputer-readable medium. In some aspects, the input 106 may alsoinclude a microphone or other sensor.

In addition to being configured to carry out steps for operating thesystem 100 using instructions stored in the memory 104, the processor102 may be configured to identify and/or characterize a physicalactivity of an individual, in accordance with the present disclosure.Specifically, the processor 102 may be configured to process imaging,and other data, obtained using the camera 110, received via input 106,or retrieved from the memory 104 or other storage location. Theprocessor 102 may also be configured to perform computations andanalyses using information provided by a user, for example, in the formof audio signals or commands, and/or inputted operations, as well asuser profile information.

In some aspects, the processor 102 may be configured to analyze a timesequence of images acquired while an individual is performing a physicalactivity or exercise. In particular, an analysis may be performed by theprocessor 102 to generate maps indicative movement of the individual,both in amplitude and direction, as shown in the examples of FIG. 1B.For example, an optical flow sensing algorithm may be applied the timesequence of images to create one or more velocity fields of theindividual's body. Such velocity fields may be utilized to identifyvarious body parts, or portions thereof, by grouping adjacent pixels inrespective maps with similar movement together, since different bodyportions perform similar movement both in direction and amplitude. Byway of example, different body parts can include the head of theindividual, the neck, the trunk, the upper, the lower arms, the hands,the upper legs, the lower legs, and feet, as well as combinationsthereof. Therefore pixels associated with the same body parts are highlycorrelated and may be grouped together. Alternative approaches tosegment different body portions can include k-means clusteringalgorithms and their variants. Also, in identifying the different bodyportions, additional information may also be provided, includingpersonal profile information, type of activity, as well as the type andtexture of clothing worn, and so forth. The processor 102 may also beconfigured to perform a number of other processing steps, includingimage processing steps.

In analyzing imaging and other data, the processor 102 may be configuredto compute various quantities associated with the identified bodyportions. In particular, the processor 102 is configured to determinethe velocity, both direction and amplitude, for the identified bodyparts, or portions thereof, as well as respective displacementamplitudes. In some aspects, vertical displacements for the differentbody portions are determined. Values for velocities or displacements foreach body portion may be determined by averaging pixel values associatedwith the body portion.

The processor 102 may then compute a number of indices associated withthe identified body portions. For example, the processor 102 may alsoinclude energy expenditure for the identified body portions. Also, theprocessor 102 may compute an intensity index using a weighted sum of thevertical displacement and a weighted sum of the square of the velocityfor each or all identified body portions. The weighting factors may beapproximated or determined based on independent measurements, as well asother provided information associated with the individual. For example,weighting factors may depend on relative mass of the individual bodyportions, gender of the individual, type of physical activity, and soforth. Weight factors may also take into account internal or externalenergy dissipation, such as joint friction, or air resistance, as wellas metabolic efficiency of converting chemical energy to mechanicalmovement. In some aspects, a repetition rate may also be determined bythe processor 102 using the identified displacements or an identifiedboundary of one or more body portion. In other implementations, theprocessor 102 may count the repetitions using a template matching of anoriented histogram of the optical flow field. As described, theprocessor 102 may compute various indices for a number of layers withincreasing level of detail.

The processor 102 may also characterize a physical activity or exerciseperformed by the individual. For instance, in some aspects, theprocessor 102 may utilize computed indices to identify an intensityand/or energy expenditure associated with the physical activity. In someaspects, the processor 102 may also identify a type of activityperformed by the individual. As mentioned, the processor 102 may furtheranalyze or correlate computed indices, and other information determinedtherefrom, with measurements physiological parameters, such as heartrate, stress, breathing rate, blood oxygen saturation, and so forth, tocharacterize the physical activity, including determining an efficiency,a stress level, an activity accuracy or a likelihood for fatigue.

Information and data processed by the processor 102 may then be relayedto the output 108 as a report. Such report may be in any form andinclude any audio or visual information associated with the analyzedphysical activity, including computed indices. For instance, the reportmay include an intensity, energy expenditure, activity duration,repetition count, and so forth, for a selected or identified physicalactivity. By way of example, FIG. 1C shows possible outputs displayed ona smartphone. In some aspects, the report may also include informationassociated with a fitness goal progress or a health condition, as wellas instructions to a user regarding the physical activity.

Referring to FIG. 1D, a video recording device 150, in accordance withaspects of the present disclosure, illustratively comprises a housing 20that encloses the circuitry and other components of the device 150.Those components include a primary circuit 22 that includes amicrocomputer based processor, one or more memory devices, along with auser interface comprising a display, a keyboard, and/or touch screen. Acamera 24 acts as a sensor for the video recording device 150. An audioinput transducer 25, such as a microphone, and an audio outputtransducer 26, such as a speaker, function as an audio interface to theuser and are connected to the primary circuitry 22. Communicationfunctions are performed through a radio frequency transceiver 28 whichincludes a wireless signal receiver and a wireless signal transmitterthat are connected to an antenna assembly 27. The video recording device150 may include a satellite positioning system (e.g., GPS, Galileo,etc.) receiver and antenna to provide position locating capabilities, aswill be appreciated by those skilled in the art. Other auxiliarydevices, such as for example, a WLAN (e.g., Bluetooth®, IEEE. 802.11)antenna and circuits for WLAN communication capabilities, also may beprovided. A battery 23 is carried within the housing 20 to supply powerto the internal circuitry and components. In some aspects, the primarycircuit 22 including the processor may be configured to operate to trackand measure the vertical movement (or other directional movement) andthe velocity (and/or acceleration) of the individual during movementusing feedback from the camera 24.

Referring now to FIG. 2A, the steps of a process 200, in accordance withaspects of the present disclosure, are shown. As indicated at processblock 202, a time sequence of images associated with an individualperforming a physical activity, such as an exercise routine, may bereceived. In some aspects, this includes operating a system or device,as described with reference to FIG. 1A-D, for acquiring such timesequence of images. In some modes of operation, a single vantage pointfor acquiring the image data may be selected for the system or device.In other modes of operation, multiple vantage points may be selected tocapture images of the entire body of an individual from multiple pointsof view, as illustrated in FIG. 2B. This may be achieved either using asingle device, or multiple devices. Although FIG. 2B shows use of thesame smartphone device, it may be appreciated that any combination ofsystems and devices, in accordance with the present disclosure may beutilized. Images may be captured either using ambient illumination, orusing light from the video recording device, or external light source.In some modes of operation, a video recording device placed may beplaced near or behind a mirror so the individual can view himself orherself during exercise. Alternatively, the time sequence of images maybe retrieved from a memory or other storage location.

Referring again to FIG. 2A, at process block 204, the time sequence ofimages may be processed and analyzed. In particular, at least one mapindicating a movement of the individual may be generated using the timesequence of images. As described, this can include utilizing an opticalflow sensing algorithm to generate various velocity field maps. Usingthe generated maps, one or more body parts, or portions thereof, maythen be identified, as indicated at process block 206.

In some aspects, characterizing physical activity may includedetermining energy expenditures, such as kinetic and/or potentialenergies, associated with various body parts or portions thereof. Thisincludes determining velocity amplitudes and velocity directions for theidentified body portions, for instance, using generated velocity maps,as well as their respective displacements, and particularly verticaldisplacements. By way of example, body portions can include the head,the neck, the trunk, the upper arms, the lower arms, the hands, theupper legs, the lower legs, and the feet of the individual, or portionsthereof.

In some aspects, a hierarchical kinematic algorithm may be utilized tocharacterize the physical activity. In particular, body motion may beanalyzed with different degrees of detail, or in layers, using variousidentified body portions. In quantifying the exercise intensity, atleast two factors may be taken into account: 1) movements of certainbody parts, such as hands and arms, require far less effort than otherparts, like the trunk, and 2) movement in the direction of gravity ornormal to the direction of gravity requires far less effort thanmovement in the opposite direction of gravity. Therefore, there is aneed to not only analyze the motion of the overall body, but alsoindividual parts of the body. This requires proper identificationindividual body parts, and analysis of the contributions of differentbody parts to the total energy expenditure of the exercise, which isperformed using hierarchical kinematic algorithm.

In particular, a hierarchical kinematic algorithm divides the body intodifferent hierarchical layers, each layer having an increasing level ofdetail of the body parts, and the contribution of each layer to thetotal energy expenditure may also increase with the layer. At thecrudest layer, the algorithm may track the overall body movement usingthe center of mass of the individual. This layer is expected tocontribute to the total energy expenditure the greatest. The algorithmmay then further analyze the head, trunk, legs, and arms of the body ata higher layer. Based on the need and image quality, the algorithm canalso analyze each body part in terms of smaller components in an evenhigher layer. To take into account the importance of gravity, thealgorithm can track not only velocity, but also movement of differentbody parts in the vertical direction. Velocity is related to kineticenergy, while vertical displacement is associated with potential energy.

By way of example, FIG. 3 shows a diagram of an analyzed exerciseactivity using two different layers, namely Layer 1 and Layer 2. InLayer 1, the individual is represented as a single object 302. On theother hand, in Layer 2, the individual is represented using major bodyparts, including arms 304, legs 306, torso 308, and head 310. As may beappreciated, any number of layers may be utilized, with the desiredlevel of detail. In addition, it is envisioned that, the number oflayers or identified body portions may depend upon the particularcharacteristics of the individual, as well as the physical activitybeing performed.

Referring again to FIG. 2A, at process block 208, one or more index maybe computed to characterize the physical activity of the individual,such as energy expenditure, intensity as well as the duration of thephysical activity being tracked. For instance, mechanical energiesassociated with different body portions may be determined. Inparticular, for an identified body portion the average kinetic energyfor each cycle of a workout is given by

$\begin{matrix}{{E_{k} = {{\frac{1}{N}{\sum\limits_{n = 1}^{N}\left( {\frac{1}{2}{m\left( {\overset{\_}{U}}_{n}^{2} \right)}} \right)}} = {m \cdot \left\lbrack {\frac{1}{N}{\sum\limits_{n = 1}^{N}\left( {\frac{1}{2}\left( {\overset{\_}{U}}_{n}^{2} \right)} \right)}} \right\rbrack}}},} & (1)\end{matrix}$

where n is the frame number, N is the total number of frames in arepetition of workout, m is the mass of the subject, and U_(n) is theaverage optical flow in frame n. In some aspects, to estimate the realvelocity of body movement from the optical flow, the height of thesubject may be used to calibrate the velocity field.

Similarly, the potential energy increase is expressed as

E _(p) =mgΔh=m·[gΔh]  (2)

where g=9.8 m/s2, the free fall acceleration, Δh is the height increase.Specifically, the height increase may be determined using

Δh=Σ _(n=1) ^(N)( v _(n) ⁺ *t ₀),  (3)

where v_(n) ⁺ is the average velocity in the upward direction from theoptical flow at frame n, and t0 is the time interval between twoadjacent frames. The total mechanical energy change per cycle is the sumof the kinetic energy (Eqn. 1) and potential energy (Eqn. 2).

In the example described above, a Layer 1 approximation focusing on thecenter of mass of an individual would not consider the detailedmovements of the body parts, which could significantly overestimate orunderestimate the actual energy expenditure. For example, in the case ofjumping jack, the subject could vigorously wave his/her arms withoutjumping much, which would affect the average body movement. This problemcould be dramatically reduced using a Layer 2 analysis, taking intoaccount the movements of major body parts. As shown in FIG. 3, the bodyparts could be segmented into head, arms, legs and trunk. The averagekinetic and potential energies of each body part may then be determinedusing an algorithm similar to the one described above. That is, theaverage kinetic and potential energies of the entire body may becomputed as weighted sum of energies of the major body parts, given by:

E _(p)=Σ_(i) w _(i) E _(p) ^(i)  (4)

E _(k)=Σ_(i) v _(i) E _(k) ^(i),  (5)

where E_(p) ^(i) is average potential energy increase during the risingportion of the workout, and E_(k) ^(i) is the average kinetic energy ofthe workout for each identified body portion. The coefficients v_(i) andw_(i) are the weighting factors that may be chosen to be the relativemass of the different body portions. For example,

E _(p,k)=0 0681E _(p,k) ^(head)+0.0943E _(p,k) ^(arms)+0.4074E _(p,k)^(legs)+0.4302E _(p,k) ^(torso)  (6)

It is anticipated, however, that the relative weight factors could alsodepend on other factors, such as the gender and possibly the type ofphysical activity being performed. Hence more complex equations, takinginto account such factors, may be used.

Computed energies, as described above, may then be utilized at processblock 208 to quantify an energy expenditure or intensity associated withthe physical activity being performed. This may include making acomparison to database listing energy expenditures or intensities ofdifferent physical activities or calibration curves. In some aspects,personal information obtained from the individual may be utilized incharacterizing the physical activity. For instance, informationassociated with a personal profile may include a resting or baselineenergy expenditure or intensity, or other baseline quantity, as well asgender, total weight, relative weights of different body portions, bodysurface area, and so forth. In some aspects, an intensity, such as alow, a medium, or a high intensity designation for the analyzed physicalactivity, may be determined using measured energy expenditure andreference data.

In addition, a number of repetitions of the physical activity may alsobe counted, for instance, by tracking a boundary associated with the atleast one body portion of the individual. For instance, a repetitioncount may be determined based on an amplitude analysis of an opticalflow field or a template matching of an oriented histogram of theoptical flow field. In addition, a duration of a physical activity, orphysical activity cycle may also be determined,

As appreciated form the above, a variety of computed indices, orquantities derived therefrom, in a variety of layers, according torequisite detail, or type of physical activity, may be computed atprocess block 208 to characterize the physical activity of the subject.For example, a metabolic equivalent of task (“MET”) quantity may becomputed using computed energies. In addition, in some aspects, suchindices, or quantities, as well as other inputted information, asdescribed, may be utilized to identify a type of physical activity beingperformed.

Then, at process block 210, a report of any form may be generated usingthe computed indices. For instance, the report may include informationassociated with intensity, energy expenditure, activity duration,repetition count, a calorie count, a metabolic index, and so forth, fora selected or identified physical activity. In some aspects, the reportmay also include information associated with a fitness goal progress ora health condition, as well as instructions to a user regarding theparticular physical activity. For example, in analyzing a particularphysical activity, the report may provide information associated with acorrectness of execution or an efficiency in energy use. In someimplementations, the report may be in the form of graphs, color maps,images, and so forth.

Referring now to FIG. 4, an oriented histogram 400 of a push-up isshown. One of the methods for correct identification of exercises isbased on a method for oriented histogram of optical flow and on theHidden Markov Model. The oriented histogram 400 plots the amplitude ofthe optical velocity field for 5 cycles. The oriented histogram 400 maybe amplified to zoom in on one push-up cycle 402. A contour plot 404 mayalso be made of the oriented histogram. The contour plot 404 may beamplified to zoom in on one push-up cycle 406.

Three methods for counting the repetitions of various exercises will nowbe disclosed. Although only three methods are presented, other methodsmay also be used. The first method is based on an amplitude analysis ofan optical flow field. Referring now to FIG. 5, charts of the amplitudeof main optical flow over time for push-ups 500, sit-ups 502, squats504, and jumping jacks 506 are shown. Referring again to FIG. 3, forsimplicity, Layer 1 may be used to count the repetitions of the exercisebecause it considers the overall body movement at each moment. In someaspects involving a periodic exercise, one cycle of the exercise can besegmented into two phases according to the main moving direction of thesubject's body, rising phase, and declining phase (or back phase andforth phase). When transferring from one phase to the other, the body'svelocity will change its direction, and the magnitude of main opticalflow will reach a minimum. Therefore, the minima of main opticalmagnitude to segment the half cycles of the exercise can be detected.Consequently, the number of cycles of the exercise can be calculated.

The second counting method is based on determining the boundary of theindividual doing the exercise. Referring to FIG. 6, determining therepetition count of an exercise using the boundary 600 of an individual602 is shown. The corresponding position change of the boundary isplotted as a function of time as shown in the chart 604. The chart 604shows periodic or quasi-periodic variations from the exerciserepetition, and those variations are counted.

The third counting method is based on a template matching method.Referring now to FIG. 7, counting the repetition count of an exerciseusing template matching is shown. A contour map 700 of a histogram maybe transformed into a plot 702 of the sum of the squared difference(“SSD”). The SSD sign may be reversed and normalized to the range [0,1], for example. Time duration of the exercise may be determined fromthe time stamps of the videos.

Generally, the difference in the oriented histograms due to the effortor exercise intensity is subtle to detect. One approach for finding thedifference includes comparing the amplitude of the optical flow field,which reflects how fast the individual moves or the kinetic energy partof the energy expenditure. However, this analysis does not count thepotential energy part of the energy expenditure or the work involved tomove the body up against gravity. The potential energy is reflected inthe y-component of the velocity field obtained with the optical flowmethod. This is because the time integral of the y-component of thevelocity is proportional to the height change of the body to body parts.

Referring now to FIG. 8, vertical displacement and velocity squareaveraged over each repetitive cycle for a sit-up 800 and a push-up 802are shown. After analyzing the body using a hierarchical algorithm,determining the velocity field of different body portions associatedwith different layers, and counting the repetitions, the physicalactivity intensity of a kinematic model is quantified. Importantparameters to be determined include the vertical displacement andvelocity square shown in the charts 804, 806 for a sit-up and a push-up.The vertical displacement is related to the potential energy, effortsconsumed to overcome gravity, and the velocity square is related to thekinetic energy. Using Layer 1, as described, the overall body velocitysquare averaged over a repetitive period can be readily determined fromthe optical flow method. For the vertical displacement, the overallvelocity in the vertical direction over time (sum over different imageframes) is integrated. Because a movement in the direction is notexpected to assume much energy, e.g., relaxing back to the flat positionin the case sit up routine, the displacement in the opposite directionof gravity only is determined. At a higher layer, the velocity squareand vertical displacement of each body part will be determined, and thephysical activity intensity is determined from the velocity square andvertical displacement of each body part.

Referring now to FIG. 9, the kinetic and potential energy analyses of astandard sit-up 900 and a non-standard sit-up 902 are shown. In astandard sit-up, the body lies flat on the floor and sits up in thevertical direction. In contrast, in a non-standard sit-up, the body doesnot relax back to the floor and does not reach the vertical directionduring the sit-up motion. The charts shown include the orientedhistogram of optical flow 904, 912; the y-component of all optical flowover time 906, 914; the amplitude of the average optical flow over time908, 916; and the potential and kinetic energy over the index of half acycle 910, 918. The potential and kinetic energy terms in the standardcase are 17.9 and 0.91 respectively. The potential and kinetic energyterms in the non-standard case are 7.69 and 0.34 respectively. As may beappreciated from the example shown, such measures and comparisons can beutilized to determine an efficiency or correctness of execution of aphysical activity.

Referring now to FIG. 10, the kinetic and potential energy analyses of astandard jumping jack 1000 and a non-standard jumping jack 1002 areshown. A non-standard jumping jack is a jumping jack where only the armsmove and not the legs. The analyses include the y-component of alloptical flow over time 1004, 1010; the amplitude of average optical flowover time 1006, 1012; and the potential and kinetic energy over theindex of half a cycle 1008, 1014. The approach described above workswell for sit-ups, push-ups and squats, but would have difficulty withexercises, such as jumping jacks. This may be appreciated from theminimal difference in results between the potential and kinetic energyfor the standard and non-standard jumping jacks 1000, 1002. Thepotential and kinetic energy terms in the standard case are 39.98 and8.81 respectively. Similarly, the potential and kinetic energy terms inthe non-standard case are 37.17 and 7.70 respectively, which are onlyslightly smaller than those of the standard jumping jacks. As such, thepotential and kinetic energy contributions from different parts of thebody may indeed need to be considered separately from the optical flowfield and counted with different weighting factors.

One approach to solving the problem associated with certain physicalactivities, such as jumping jacks, may be to consider differentcontributions of different body parts, as described, using the weightpercentages of different body parts as the weighting factors. Referringparticularly to FIG. 11, a weighted kinetic and potential energyanalysis 1100 of a standard jumping jack 1102 vs. a non-standard jumpingjack 1104 is shown. Both the potential and kinetic energy terms showmuch larger differences between standard and non-standard jumping jackswhen compared to the un-weighted potential and kinetic energy analysisshown in FIG. 10. This is because of a large difference in the movementof the trunk part of the body between the standard and non-standardjumping jacks.

The weighted potential-kinetic energy analysis can accurately analyzethe effort or intensity of an exercise routine. Together with thecounting of repetition and duration, the analysis can provide anestimate of energy expenditure during the exercise. However, to convertthe weighted potential and kinetic energies into energy expenditure,calibration might be needed. It is envisioned that one way to obtainsuch calibration would be to correlate the potential and kineticenergies obtained from the image processing, in accordance with thepresent disclosure, with the energy expenditure obtained with anotherapparatus, such as one that measures produced carbon dioxide andconsumed oxygen, known as indirect calorimetry. This calibration mayvary from individual to individual, which can be taken into accountusing either the individual's gender and body weight (or body surfacearea) or resting energy expenditure as normalization factors. In thisway, once the individual enters his or her personal profile and the typeof exercise is identified, the energy expenditure can be determinedbased on the video using the weighted potential-kinetic energy analysisalgorithm.

The present invention has been described in terms of one or moreembodiments, including preferred embodiments, and it should beappreciated that many equivalents, alternatives, variations, andmodifications, aside from those expressly stated, are possible andwithin the scope of the invention.

As used in the claims, the phrase “at least one of A, B, and C” means atleast one of A, at least one of B, and/or at least one of C, or any oneof A, B, or C, or a combination of A, B, or C. A, B, and C are elementsof a list, and A, B, and C may be anything contained in theSpecification.

What is claimed is:
 1. A system for analyzing a physical activity of anindividual without contacting the individual, the system comprising: anapparatus configured to capture a time sequence of images of anindividual performing a physical activity; and a processor configuredto: receive the captured time sequence of images; generate, using thecaptured time sequence of images, one or more measures indicating motionassociated with the physical activity performed by the individual;identify at least one body portion of the individual using the one ormore measures indicating motion associated with the physical activityperformed by the individual; compute at least one index associated withthe at least one identified body portion that quantifies the performanceof the physical activity; and generate a report using the at least oneindex.
 2. The system of claim 1, wherein the one or more measuresincludes a map, and wherein the map comprises one or more velocityfields indicative of motion associated with the physical activityperformed by the individual.
 3. The system of claim 2, wherein theprocessor is further configured to utilize an optical flow sensingalgorithm to generate at least one map.
 4. The system of claim 2,wherein the processor is further configured to determine at least one ofa velocity amplitude and a velocity direction for the at least one bodyportion using at least one map.
 5. The system of claim 1, wherein theone or more measures includes a vertical displacement of at least onebody portion of the individual using the captured time sequence ofimages.
 6. The system of claim 1, wherein the at least one indexincludes at least one of an energy expenditure and an intensity.
 7. Thesystem of claim 6, wherein the processor is further configured tocompute the energy expenditure of the physical activity using a weightedsum of a vertical displacement and a velocity amplitude square of atleast one body portion averaged over a duration of the physicalactivity.
 8. The system of claim 1, wherein the processor is furtherconfigured to determine the at least one index using a hierarchicalalgorithm.
 9. The system of claim 1, wherein the at least one bodyportion includes at least one of a head of the individual, a neck of theindividual, a trunk of the individual, upper arms of the individual,lower arms of the individual, hands of the individual, upper legs of theindividual, lower legs of the individual, and feet of the individual.10. The system of claim 1, wherein the processor is further configuredto count repetitions of the physical activity by tracking a boundaryassociated with the at least one body portion of the individual.
 11. Thesystem of claim 1, wherein the processor is further configured to countrepetitions of the physical activity based on an amplitude analysis ofan optical flow field.
 12. The system of claim 1, wherein the processoris further configured to count repetitions of the physical activitybased on a template matching of an oriented histogram of an optical flowfield.
 13. The system of claim 1, wherein the processor is furtherconfigured for analysis of health parameters related to the physicalactivity based on the at least one index that quantifies the performanceof the physical activity, the analysis of health parameters informativeas to a predefined fitness goal or progress related to a healthcondition.
 14. The system of claim 1, wherein the processor is furtherconfigured to combine the at least one index that quantifies theperformance of the physical activity with one or more measuredphysiological parameters to assess a predetermined correctness ofexecution.
 15. The system of claim 1, wherein the processor is furtherconfigured to combine the at least one index that quantifies theperformance of the physical activity with one or more measuredphysiological parameters informative as to a predefined fitness goal orprogress related to a health condition.
 16. The system of claim 1,wherein the processor is further configured for analysis of workerefficiency based on the at least one index that quantifies theperformance of the physical activity.
 17. The system of claim 1, whereinthe processor is further configured for determining a likelihood offatigue based on the at least one index that quantifies the performanceof the physical activity.
 18. The system of claim 1, wherein theprocessor is further configured for predicting a stress level of theindividual based on the at least one index that quantifies theperformance of the physical activity
 19. The system of claim 1, whereinthe processor is further configured for providing an indication of alevel of health or health condition.
 20. The system of claim 1, furthercomprising: at least one sensor in operable communication with theprocessor, the at least one sensor providing data includingphysiological information to the processor.